
The connection of the physical propagation mechanisms present in a channel with the architecture and the volume of training data required for the development of an ML-based channel model.

The potential of ML methods to simplify the input required for the development of PL models in complex environments (e.g. replacing elaborate CAD models with a sequence of images of the environment to be processed by the model).

Further research on GANs and how they can improve the accuracy of an ML propagation model, especially in the regime of low-volume training data.

Reinforcement learning (RL [1]}) (being the third class of ML-related problems, apart from supervised and unsupervised ones) has been used for wireless channel modeling, tackling tasks such as interference mitigation [2]} or resource and channel allocation [3]}. Investigation of RL techniques for electromagnetic wave propagation modeling seems highly promising.

